TIRE-seq Dendritic cells Flt3 stimulation

Recap

Prior 96w evaluation of TurboCapture-Seq v2 showed low UMIs recovered and high seq saturation. I did low throughput troubleshooting and didn’t see any issues. Process this experiment myself taking care to remove residual liquids from wash steps.

Process Hui Shi of Naik lab + Flt3 timecourse. Includes a few of my samples.

Samples

Sorted dendritic cells
HEK293T cell lysates in 1x TCL @ cells/uL
PBMC cell lysates in 1x TCL @ 500 cells/uL
No template control 1x TCL

Notebook recap

SCE object in generate in 1A_generateSCE_reads notebook. The samples were then clustered in the 2B Clustering Wt notebook

Notebook aim

Compare different subsets sorted from dendritic cells at 5 and 7 days. The easiest contrasts to interpret are the difference from the common dendritic cell precursor (CDP).

Workflow is from:
https://bioconductor.org/packages/devel/workflows/vignettes/RNAseq123/inst/doc/limmaWorkflow.html#differential-expression-analysis

Read SCE and preprocessing

This was generated in notebook 2B.
For the comparision of pre-pDC-like cells with the mature subsets I am not interested in the day 7 timepoint. Remove these samples.

sce <- readRDS(here::here(
   "data/TIRE_dendritic_mouse/SCEs", "DCs_cluster.sce.rds"))

sce$Time_day <- paste(sce$Cell_type, sce$Timepoint_Day, sep="_")
sce <- sce[,sce$Time_day != "pre-pDC_like_7"]

dge <- scran::convertTo(sce, type="edgeR")

Have a look at the important metadata.
There are not enough replicates for the preDC contrast to be meaningful.

tb <- dge$samples[,c("Cell_type", "Cell_number", "Ligand", "Timepoint_Day")]

tb %>% 
  dplyr::count(Cell_type, Ligand, Timepoint_Day)

Recap the PCA

The cell type is the major difference between samples with the timepoint being much less so.

plt1 <- scater::plotPCA(sce,colour_by="Cell_type", shape_by= "Timepoint_Day") + 
  guides(
  color = guide_legend(ncol = 2),
  shape = guide_legend(ncol = 2)
) +
  theme_Publication()

plt1

Write the day as text.

pca_tb <- as_tibble(reducedDim(sce))
pca_tb$Cell_Type <- sce$Cell_type
pca_tb$Timepoint <- sce$Timepoint_Day
pca_tb$Timepoint <- as.factor(pca_tb$Timepoint)

plt2 <- ggplot(pca_tb, aes(x=PC1, y=PC2, colour=Cell_Type, label=Timepoint)) +
  geom_text(size=5) +
  xlab("PC1 (34%)") + ylab("PC2 (28%)") +
  scale_colour_brewer(palette = "Dark2") +
  theme_Publication()
  
plt2

Write the day as dots

plt3 <- ggplot(pca_tb, aes(x=PC1, y=PC2, colour=Cell_Type)) +
  geom_point(size=3) +
  xlab("PC1 (34%)") + ylab("PC2 (28%)") +
  scale_colour_brewer(palette = "Dark2") +
  theme_Publication()
  
plt3

Filter low expressed genes

Doing this reduces the multiple testing burden and fits variation better.

dim(dge)

## [1] 21484    28

keep.exprs <- filterByExpr(dge, group=dge$samples$Timepoint_Day, min.count=1)
dge <- dge[keep.exprs,, keep.lib.sizes=FALSE]
dim(dge)

## [1] 10612    28

Model timepoint as a numeric

unique(dge$samples$Timepoint_Day)

## [1] 5 0 1 7
## Levels: 0 1 5 7

dge$samples$Day <- as.numeric(dge$samples$Timepoint_Day)

dge$samples$Day <- ns(dge$samples$Day, df = 2)

Design matrix

Build the model matrix.
Regress out the effect of the timepoint as this is minor compared to the cell type subsets.

sm <- model.matrix(~0+Day + Cell_type, data=dge$samples)
head(sm)

##                 Day1       Day2 Cell_typecDC2 Cell_typeCDP Cell_typepDC
## AACGAGCCTG 0.5689662 -0.0933913             0            0            0
## ACAGTAGCTC 0.0000000  0.0000000             0            0            0
## AGACGCATCG 0.3997543 -0.2581491             0            0            0
## AGTTCCGTGA 0.5689662 -0.0933913             0            0            0
## ATCAAGAACG 0.5689662 -0.0933913             0            1            0
## ATCAGCGCGA 0.3923644  0.7057267             1            0            0
##            Cell_typepre-pDC_like Cell_typepreDC Cell_typeTotal_DC_culture
## AACGAGCCTG                     1              0                         0
## ACAGTAGCTC                     0              0                         1
## AGACGCATCG                     0              0                         1
## AGTTCCGTGA                     1              0                         0
## ATCAAGAACG                     0              0                         0
## ATCAGCGCGA                     0              0                         0

# hypens not allowed
colnames(sm) <- make.names(colnames(sm), unique = FALSE, allow_ = TRUE)

Decide on the contrasts. I got this answer from Perplexity ai:

The CDP (Common Dendritic cell Progenitor) is a key cell type in the dendritic cell lineage development:

The CDP is the myeloid-derived precursor that gives rise to the preDC (pre-Dendritic Cell) population, which can then further differentiate into conventional dendritic cells (cDCs) and plasmacytoid dendritic cells (pDCs).
CDPs are the upstream precursor of preDCs, and can differentiate into both the preDC and pDC lineages.
CDPs are considered the common or conventional dendritic cell progenitor, as they give rise to the cDC lineage, in contrast to the distinct pDC precursor population.

This matches with the PCA where cDC samples were the most distinct.

contr.matrix <- makeContrasts(
   CDPvspDC = Cell_typepDC - Cell_typeCDP, 
   CDPvscDC2 =  Cell_typecDC2 - Cell_typeCDP,
   cDC2vcpDC =  Cell_typepDC - Cell_typecDC2,
   cDC2vspDClike = Cell_typecDC2 - Cell_typepre.pDC_like,
   pDCvspDClike = Cell_typepDC - Cell_typepre.pDC_like,
   levels = colnames(sm))

contr.matrix %>% 
  kable()

	CDPvspDC	CDPvscDC2	cDC2vcpDC	cDC2vspDClike	pDCvspDClike
Day1	0	0	0	0	0
Day2	0	0	0	0	0
Cell_typecDC2	0	1	-1	1	0
Cell_typeCDP	-1	-1	0	0	0
Cell_typepDC	1	0	1	0	1
Cell_typepre.pDC_like	0	0	0	-1	-1
Cell_typepreDC	0	0	0	0	0
Cell_typeTotal_DC_culture	0	0	0	0	0

In each contrast, the format is A - B where:

A represents the condition considered as the “treatment” or point of interest
B represents the condition considered as the “control” or baseline

Limm voom

Fit this model using limma voom.
The final model looks good.

par(mfrow=c(1,2))
v <- voom(dge, sm, plot=TRUE)

vfit <- lmFit(v, sm)
vfit <- contrasts.fit(vfit, contrasts=contr.matrix)
efit <- eBayes(vfit)
plotSA(efit, main="Final model: Mean-variance trend")

Differential expression analysis

Check how many genes are differentially expressed

summary(decideTests(efit))

##        CDPvspDC CDPvscDC2 cDC2vcpDC cDC2vspDClike pDCvspDClike
## Down        585       832       227           191           30
## NotSig     9420      8413     10216          9867        10534
## Up          607      1367       169           554           48

Write results

The write.fit function can be used to extract and write results for all three comparisons to a single output file.

write.fit(efit, file=here::here(
  "data/TIRE_dendritic_mouse//Matrices/celltype_de_results.txt"
  ))

saveRDS(efit, here::here(
  "data/TIRE_dendritic_mouse/Matrices/celltype_dc_efit.rds"
))

Investigate DE genes between pDC and cDC2

These are the 2 most mature subsets.

The following info from Miltenyi

pDCs are primarily located in blood and lymphoid tissues. They depend on the E2-2 transcription factor and express B220, Siglec-H, mPDCA-1 (CD317 or Bst2), as well as intermediate levels of MHC class II, CD11c, and costimulatory molecules. pDCs are poor stimulators of T helper (TH) cells, but upon stimulation with bacterial DNA containing particular unmethylated CpG motifs or upon viral challenge, they produce large amounts of type I IFN and acquire antigen-presenting capacity (PMID: 16172135, 15728491).

Upon pathogen encounter, pDCs produce large amounts of type I IFN and acquire antigen-presenting capacity.

cDC2 exhibit the same MHC class II and CD11c expression pattern as cDC1, but express additional markers not present on cDC1 and depend on a different transcription factor, i.e., IRF4. Resident spleen and lymph node cDC2 express CD4 and SIRPα. Migratory cDC2 that infiltrate lymph nodes can be distinguished from resident cDCs under non-inflammatory conditions by the expression of MHC class II, CD11c, and peripheral and migratory markers (e.g. CCR7 and maturation markers). cDC2 induce different responses, such as activation of ILC2 and TH2 cells against parasites and during asthma, and induction of ILC3 and TH17 immune responses to extracellular bacteria (PMID: 27760337).

cDC2 activate innate lymphoid cells 2 (ILC2) and TH2 cells and Induce ILC3 and TH17 immune responses.

cDC2vcpDC <- topTable(efit, coef=3, n=length(efit$genes$ID), sort.by = "logFC")
results <- as_tibble(cDC2vcpDC)
results$ID <- rownames(cDC2vcpDC)

# add a column of NAs
results$DElabel <- "n/s"
# if log2Foldchange > 1 or < -1 and pvalue < 0.05, set as "UP"
results$DElabel[results$logFC > 1 & results$adj.P.Val < 0.1] <- "pDC"
results$DElabel[results$logFC < -1 & results$adj.P.Val < 0.1] <- "cDC2"

Add gene labels

results$genelabels <- ""
results$genelabels[results$ID == "Ly6d"] <- "Ly6d"
results$genelabels[results$ID == "Ccr9"] <- "Ccr9"
results$genelabels[results$ID == "Cd9"] <- "Cd9"
results$genelabels[results$ID == "Anxa1"] <- "Anxa1"
results$genelabels[results$ID == "Anxa2"] <- "Anxa2"
results$genelabels[results$ID == "Siglech"] <- "Siglech"
results$genelabels[results$ID == "Ctsl"] <- "Ctsl"
results$genelabels[results$ID == "Lyz2"] <- "Lyz2"

Gene interpretation

Ly6d is expressed in dendritic cells, particularly in plasmacytoid dendritic cells (pDCs) and their precursors.
Ly6D is highly expressed in the Ly6D+Siglec-H+ precursor population in mouse bone marrow that gives rise to both conventional dendritic cells (cDCs) and pDCs.
ANXA1 acts as an endogenous brake on DC maturation and function, preventing excessive activation of adaptive immunity
ANXA2 expressed by tumor cells like NPC suppresses DC function, leading to an immunosuppressive microenvironment that favors tumor growth

Volcano

plt3 <- ggplot(data=results, aes(x=logFC, y=-log10(adj.P.Val), colour=DElabel, label=genelabels)) + 
  geom_point(alpha=0.33, size=1.5) +
  geom_text_repel(size=4, colour="black") +
  guides(colour = guide_legend(override.aes = list(size=3, alpha=1))) +
  scale_color_manual(values = c("darkblue", "grey", "darkorange"), name = "Upregulated") +
  geom_vline(xintercept = 1, linetype="dotted") + 
  geom_vline(xintercept = -1, linetype="dotted") +
  theme_Publication()
plt3

MAplot

plt2 <- ggplot(data=results, aes(x=AveExpr, y=logFC, colour=DElabel)) + 
  geom_point(alpha=0.75, size=1.5) +
  guides(colour = guide_legend(override.aes = list(size=3, alpha=1))) +
  ylab("Log fold change") + xlab("Log counts per million") +
  scale_color_manual(values = c("darkblue", "grey", "darkorange"), name = "Upregulated") +
  theme_Publication()

plt2

Gene set testing

Need to convert geneIDs from ensembl to enterez

geneids <- as.data.frame(v$genes$ID)
colnames(geneids) <- "ENSEMBL"

geneids$entrez <- mapIds(org.Mm.eg.db, keys = geneids$ENSEMBL, keytype = "ENSEMBL", column = "ENTREZID")

Look at H hallmark gene sets. Only EMT and apoptosis is different and barely so.

load("data/MSigDB/mouse_H_v5p2.rdata")
idx <- ids2indices(Mm.H,identifiers = geneids$entrez)
cam.pDC.cDC2 <- camera(v,idx,sm,contrast=contr.matrix[,4])
head(cam.pDC.cDC2,10)

Visualize as a barplot

geom_GeneSet_Barchart(cam.pDC.cDC2) + theme_Publication(base_size = 15)

Investigate DE genes pDCs from pDC-like

I checked the literature and there is some unknowns about the pre-pDC-like subset and how comitted they are to the pDC lineage.
I have samples of pre-pDCs so may as well investigate this difference.

cDC2 vs pre-pDC-like

cDC2vspDClike <- topTable(efit, coef=4, n=length(efit$genes$ID), sort.by = "logFC")
results <- as_tibble(cDC2vspDClike)
results$ID <- rownames(cDC2vspDClike)

# add a column of NAs
results$DElabel <- "n/s"
# if log2Foldchange > 1 or < -1 and pvalue < 0.05, set as "UP"
results$DElabel[results$logFC > 1 & results$adj.P.Val < 0.1] <- "cDC2"
results$DElabel[results$logFC < -1 & results$adj.P.Val < 0.1] <- "pre-pDC-like"

Add gene labels

results$genelabels <- ""
results$genelabels[results$ID == "Lpl"] <- "Lpl"
results$genelabels[results$ID == "Gpnmb"] <- "Gpnmb"
results$genelabels[results$ID == "Mpo"] <- "Mpo"
results$genelabels[results$ID == "Lyz2"] <- "Lyz2"

results$genelabels[results$ID == "Atp1b1"] <- "Atp1b1"
results$genelabels[results$ID == "Ly6d"] <- "Ly6d"
results$genelabels[results$ID == "Atp2b4"] <- "Atp2b4"
results$genelabels[results$ID == "Irf8"] <- "Irf8"

Volcano

plt4 <- ggplot(data=results, aes(x=logFC, y=-log10(adj.P.Val), colour=DElabel, label=genelabels)) + 
  geom_point(alpha=0.33, size=1.5) +
  geom_text_repel(size=4, colour="black") +
  guides(colour = guide_legend(override.aes = list(size=3, alpha=1))) +
  scale_color_manual(values = c("darkblue", "grey", "darkorange"), name = "Upregulated") +
  geom_vline(xintercept = 1, linetype="dotted") + 
  geom_vline(xintercept = -1, linetype="dotted") +
  theme_Publication()

plt4

pDC vs pre-pDC-like

pDCvspDClike <- topTable(efit, coef=5, n=length(efit$genes$ID), sort.by = "logFC")
results <- as_tibble(pDCvspDClike)
results$ID <- rownames(pDCvspDClike)

# add a column of NAs
results$DElabel <- "n/s"
# if log2Foldchange > 1 or < -1 and pvalue < 0.05, set as "UP"
results$DElabel[results$logFC > 1 & results$adj.P.Val < 0.1] <- "pDC"
results$DElabel[results$logFC < -1 & results$adj.P.Val < 0.1] <- "pre-pDC-like"

Add gene labels

results$genelabels <- ""
results$genelabels[results$ID == "Gpnmb"] <- "Gpnmb"
results$genelabels[results$ID == "Ccr9"] <- "Ccr9"
results$genelabels[results$ID == "Sh3bp2"] <- "Sh3bp2"
results$genelabels[results$ID == "Ly6a"] <- "Ly6a"

results$genelabels[results$ID == "Bub1"] <- "Bub1"
results$genelabels[results$ID == "Gem"] <- "Gem"
results$genelabels[results$ID == "Il12a"] <- "Il12a"
results$genelabels[results$ID == "Ccnd2"] <- "Ccnd2"

Volcano

plt5 <- ggplot(data=results, aes(x=logFC, y=-log10(adj.P.Val), colour=DElabel, label=genelabels)) + 
  geom_point(alpha=0.33, size=1.5) +
  geom_text_repel(size=4, colour="black") +
  guides(colour = guide_legend(override.aes = list(size=3, alpha=1))) +
  scale_color_manual(values = c("grey", "darkblue", "darkorange"), name = "Upregulated") +
  geom_vline(xintercept = 1, linetype="dotted") + 
  geom_vline(xintercept = -1, linetype="dotted") +
  theme_Publication()

plt5

Investigate pre-pDC-like differences from pDCs and cDC2s

Something of interest would be common genes that go up in the differentiation from CDP in both cell types. This would not be evident in a head to head DE but can be inferred by comparing theeir DE from the CDP state.

summary(decideTests(efit)) %>% 
  kable()

	CDPvspDC	CDPvscDC2	cDC2vcpDC	cDC2vspDClike	pDCvspDClike
Down	585	832	227	191	30
NotSig	9420	8413	10216	9867	10534
Up	607	1367	169	554	48

cDC2vcpDC <- topTable(efit, coef=3, n=length(efit$genes$ID), sort.by = "logFC", p.value = 0.05, lfc=1)
cDC2vpDClike <- topTable(efit, coef=4, n=length(efit$genes$ID), sort.by = "logFC", p.value = 0.05, lfc=1)
pDCvpDClike <- topTable(efit, coef=5, n=length(efit$genes$ID), sort.by = "logFC", p.value = 0.05, lfc=1)

Find genes that are common DE in pDC and cDC2 as they differentiate from CDPs.

common <- intersect(row.names(cDC2vpDClike),
                          row.names(pDCvpDClike))

Venn diagram

Interpretation here is:

cDC2s more distinct from pre-pDC-like cells than pDCs in terms of DE genes
- This fits with the naming convention
There are only 4 genes DE in all conditions

venn_list <- list(
  "cDC2 vs pDC" = row.names(cDC2vcpDC), 
  "cDC2 vs pre-pDC-like" = row.names(cDC2vpDClike),
  "pDC vs pre-pDC-like" = row.names(pDCvpDClike)
  )

ggvenn(venn_list,
       show_elements = F, label_sep = "\n",
       text_size = 8,
       show_percentage = FALSE,
       fill_color = c("navy", "springgreen4", "orchid1")
       )

Statistical test of DE genes pDC vs cDC2

The following code was written by Claude.ai.
Its recmondadtion was Chi Squared test

# Create the observed frequencies
group1 <- 381  # cDC2 vs pDC total (216 + 128 + 33 + 4)
group2 <- 630  # cDC2 vs pre-pDC-like total (467 + 128 + 31 + 4)

# Create a matrix of observed frequencies
observed <- c(group1, group2)
names(observed) <- c("cDC2_vs_pDC", "cDC2_vs_prePDClike")

# Perform chi-square test
result <- chisq.test(observed)

# Print results
print(result)

## 
##  Chi-squared test for given probabilities
## 
## data:  observed
## X-squared = 61.326, df = 1, p-value = 4.836e-15

# Calculate proportion in each group for interpretation
total <- sum(observed)
prop1 <- group1/total
prop2 <- group2/total

cat("\nProportions:\n")

## 
## Proportions:

cat("cDC2 vs pDC:", round(prop1 * 100, 2), "%\n")

## cDC2 vs pDC: 37.69 %

cat("cDC2 vs pre-pDC-like:", round(prop2 * 100, 2), "%\n")

## cDC2 vs pre-pDC-like: 62.31 %

What are DE genes in all conditions

all_de <- intersect(common, row.names(cDC2vcpDC))

View their expression in a boxplot

Pretty much all in the order of CDP > cDC2 > pDC

Must be a kind of step-wise increase in gene expression between cell types.

plotExpression(sce[,sce$Cell_type %in% c("cDC2", "pDC", "pre-pDC_like")], all_de, colour_by = "Cell_type", point_size=2) +
  theme_Publication(base_size = 16)

Alternate boxplot

Try a visualization with x axis as cell type

dc_counts <- as_tibble(
  as.matrix(counts(sce[all_de,sce$Cell_type %in% c("cDC2", "pDC", "pre-pDC_like")])), 
  rownames = "gene_id"  # This creates a new column called 'row_id' with the rownames
) %>% 
  pivot_longer(-gene_id)

dc_counts <- left_join(dc_counts, dge$samples,
          by = c("name" = "Well_BC"))

dc_counts$Cell_type <- recode(dc_counts$Cell_type,
                            "pre-pDC_like" = "pre-pDC-like")

dc_counts$Cell_type <- factor(dc_counts$Cell_type, 
                        levels = c("pre-pDC-like", "pDC", "cDC2"))

View as a boxplot

ggplot(data=dc_counts, 
               aes(x=Cell_type, y=value+1, fill=gene_id)) +
  geom_boxplot() +
  scale_y_continuous(trans='log10') +
  annotation_logticks(base = 10, sides = "l") +
  scale_fill_brewer(type="Qualitative", palette = "Set2") +
  xlab("") + ylab("Expression(logcount)")

I don’t like this either. Try a wrapped dot plot

ggplot(data=dc_counts, 
               aes(x=Cell_type, y=value+1, colour=Cell_type)) +
  geom_boxplot() + geom_jitter() +
  scale_y_continuous(trans='log10') +
  annotation_logticks(base = 10, sides = "l") +
  scale_colour_manual(values = c(
    "cDC2" = "#228B22",        # Forest green
    "pDC" = "#4B0082",         # Dark purple
    "pre-pDC-like" = "#FF1493"
  )) +
  xlab("") + ylab("Expression (logcounts)") +
  facet_wrap(~ gene_id, scales = "free_y", labeller = labeller(ID = label_value)) +
  theme(strip.text = element_text(face = "bold.italic"))

I like the wrapped boxplot

Conclusion

The results make sense to me. Will confirm with the domain experts.

Session info

sessionInfo()

## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Red Hat Enterprise Linux 9.4 (Plow)
## 
## Matrix products: default
## BLAS:   /stornext/System/data/software/rhel/9/base/tools/R/4.4.1/lib64/R/lib/libRblas.so 
## LAPACK: /stornext/System/data/software/rhel/9/base/tools/R/4.4.1/lib64/R/lib/libRlapack.so;  LAPACK version 3.12.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: Australia/Melbourne
## tzcode source: system (glibc)
## 
## attached base packages:
##  [1] grid      splines   stats4    stats     graphics  grDevices utils    
##  [8] datasets  methods   base     
## 
## other attached packages:
##  [1] ggthemes_5.1.0              here_1.0.1                 
##  [3] org.Mm.eg.db_3.19.1         AnnotationDbi_1.66.0       
##  [5] viridis_0.6.5               viridisLite_0.4.2          
##  [7] ggrepel_0.9.6               ggvenn_0.1.10              
##  [9] knitr_1.48                  patchwork_1.3.0            
## [11] edgeR_4.2.2                 limma_3.60.6               
## [13] scater_1.32.1               scran_1.32.0               
## [15] scuttle_1.14.0              lubridate_1.9.3            
## [17] forcats_1.0.0               stringr_1.5.1              
## [19] dplyr_1.1.4                 purrr_1.0.2                
## [21] readr_2.1.5                 tidyr_1.3.1                
## [23] tibble_3.2.1                ggplot2_3.5.1              
## [25] tidyverse_2.0.0             SingleCellExperiment_1.26.0
## [27] SummarizedExperiment_1.34.0 Biobase_2.64.0             
## [29] GenomicRanges_1.56.2        GenomeInfoDb_1.40.1        
## [31] IRanges_2.38.1              S4Vectors_0.42.1           
## [33] BiocGenerics_0.50.0         MatrixGenerics_1.16.0      
## [35] matrixStats_1.4.1          
## 
## loaded via a namespace (and not attached):
##  [1] DBI_1.2.3                 gridExtra_2.3            
##  [3] rlang_1.1.4               magrittr_2.0.3           
##  [5] RSQLite_2.3.7             compiler_4.4.1           
##  [7] DelayedMatrixStats_1.26.0 png_0.1-8                
##  [9] vctrs_0.6.5               pkgconfig_2.0.3          
## [11] crayon_1.5.3              fastmap_1.2.0            
## [13] XVector_0.44.0            labeling_0.4.3           
## [15] utf8_1.2.4                rmarkdown_2.28           
## [17] tzdb_0.4.0                UCSC.utils_1.0.0         
## [19] ggbeeswarm_0.7.2          bit_4.5.0                
## [21] xfun_0.48                 bluster_1.14.0           
## [23] zlibbioc_1.50.0           cachem_1.1.0             
## [25] beachmat_2.20.0           jsonlite_1.8.9           
## [27] blob_1.2.4                highr_0.11               
## [29] DelayedArray_0.30.1       BiocParallel_1.38.0      
## [31] irlba_2.3.5.1             parallel_4.4.1           
## [33] cluster_2.1.6             R6_2.5.1                 
## [35] RColorBrewer_1.1-3        bslib_0.8.0              
## [37] stringi_1.8.4             jquerylib_0.1.4          
## [39] Rcpp_1.0.13               Matrix_1.7-0             
## [41] igraph_2.0.3              timechange_0.3.0         
## [43] tidyselect_1.2.1          rstudioapi_0.17.0        
## [45] abind_1.4-8               yaml_2.3.10              
## [47] codetools_0.2-20          lattice_0.22-6           
## [49] KEGGREST_1.44.1           withr_3.0.1              
## [51] evaluate_1.0.1            Biostrings_2.72.1        
## [53] pillar_1.9.0              generics_0.1.3           
## [55] rprojroot_2.0.4           hms_1.1.3                
## [57] sparseMatrixStats_1.16.0  munsell_0.5.1            
## [59] scales_1.3.0              glue_1.8.0               
## [61] metapod_1.12.0            tools_4.4.1              
## [63] BiocNeighbors_1.22.0      ScaledMatrix_1.12.0      
## [65] locfit_1.5-9.10           cowplot_1.1.3            
## [67] colorspace_2.1-1          GenomeInfoDbData_1.2.12  
## [69] beeswarm_0.4.0            BiocSingular_1.20.0      
## [71] vipor_0.4.7               cli_3.6.3                
## [73] rsvd_1.0.5                fansi_1.0.6              
## [75] S4Arrays_1.4.1            gtable_0.3.5             
## [77] sass_0.4.9                digest_0.6.37            
## [79] SparseArray_1.4.8         dqrng_0.4.1              
## [81] farver_2.1.2              memoise_2.0.1            
## [83] htmltools_0.5.8.1         lifecycle_1.0.4          
## [85] httr_1.4.7                statmod_1.5.0            
## [87] bit64_4.5.2

DCs cell type DE analysis

Daniel Brown

2024-11-21

TIRE-seq Dendritic cells Flt3 stimulation

Recap

Notebook recap

Notebook aim

Read SCE and preprocessing

Recap the PCA

Filter low expressed genes

Design matrix

Limm voom

Differential expression analysis

Write results

Investigate DE genes between pDC and cDC2

Gene interpretation

Volcano

MAplot

Gene set testing

Investigate DE genes pDCs from pDC-like

cDC2 vs pre-pDC-like

Volcano

pDC vs pre-pDC-like

Volcano

Investigate pre-pDC-like differences from pDCs and cDC2s

Venn diagram

Statistical test of DE genes pDC vs cDC2

View their expression in a boxplot

Alternate boxplot

Conclusion

Session info